Here is the corrected implementation of the `JobQueue` class with proper thread-safe locking to prevent race conditions:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            result = processor(data)
            self.results[job_id] = result
            del self.jobs[job_id]

        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:

1. **Added a `threading.Lock`** to synchronize access to shared data structures.
2. **Protected all modifications to `self.jobs` and `self.results`** with the lock using `with self.lock:`.
3. **Ensured atomicity of job processing** by including the entire job handling logic (read, process, write, delete) under a single lock to prevent concurrent interference.

### Why This Fix Works:

- **Prevents data races:** Only one thread can access or modify the job queue or results at a time.
- **Ensures consistency:** Job removal and result writing are done atomically, avoiding partial updates.
- **Maintains thread safety:** All public methods are now protected against concurrent access.

### Optional Optimization (Advanced):

If the `processor(data)` function is CPU-intensive and you want to avoid locking during processing, you can move that step outside the critical section:

```python
def process_job(self, job_id, processor):
    with self.lock:
        if job_id not in self.jobs:
            return False

        data = self.jobs[job_id]
        del self.jobs[job_id]

    result = processor(data)

    with self.lock:
        self.results[job_id] = result

    return True
```

This version reduces the time threads spend holding the lock, improving performance in high-concurrency scenarios while still maintaining correctness.